import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				def __init__(		self						,      lowercase						,      lowercase						,      lowercase=1_0_2_4						,      lowercase=1_0_2_4						,      lowercase=3.6   ):



						"""simple docstring"""





						A_						:							Any      = tokenizer
						A_						:							Union[str, Any]      = tokenizer.bos_token_id
						A_						:							List[str]      = dataset
						A_						:							Optional[Any]      = seq_length
						A_						:							List[Any]      = seq_length * chars_per_token * num_of_sequences







				def __iter__(		self   ):



						"""simple docstring"""





						A_						:							Tuple      = iter(self.dataset   )
						A_						:							Dict      = True
						while more_examples:
								A_  ,     A_						:							List[str]      = [], 0
								while True:
										if buffer_len >= self.input_characters:
												break
										try:
												buffer.append(next(lowercase   )['content']   )
												buffer_len += len(buffer[-1]   )
										except StopIteration:
												A_						:							Union[str, Any]      = False
												break
								A_						:							List[str]      = tokenizer(lowercase						,      truncation=lowercase   )['input_ids']
								A_						:							List[Any]      = []
								for tokenized_input in tokenized_inputs:
										all_token_ids.extend(tokenized_input + [self.concat_token_id]   )
								for i in range(0						,      len(lowercase   )						,      self.seq_length   ):
										A_						:							str      = all_token_ids[i : i + self.seq_length]
										if len(lowercase   ) == self.seq_length:
												yield torch.tensor(lowercase   )




def 							UpperCamelCase		(			__lowercase		:					List[str]      ):
		'''simple docstring'''




		A_						:							Tuple      = {'streaming': True}
		A_						:							Dict      = load_dataset(args.dataset_name		,split='train'		,**__lowercase      )
		A_						:							List[str]      = ConstantLengthDataset(__lowercase		,__lowercase		,seq_length=args.seq_length      )
		A_						:							Tuple      = DataLoader(__lowercase		,batch_size=args.batch_size      )
		return eval_dataloader




def 							UpperCamelCase		(			__lowercase		:					Dict      ):
		'''simple docstring'''




		model.eval()
		A_						:							Dict      = []
		for step, batch in enumerate(__lowercase      ):
				with torch.no_grad():
						A_						:							Dict      = model(__lowercase		,labels=__lowercase      )
				A_						:							Any      = outputs.loss.repeat(args.batch_size      )
				losses.append(accelerator.gather(__lowercase      )      )

				if args.max_eval_steps > 0 and step >= args.max_eval_steps:
						break
		A_						:							int      = torch.mean(torch.cat(__lowercase      )      )
		try:
				A_						:							int      = torch.exp(__lowercase      )
		except OverflowError:
				A_						:							int      = float('inf'      )
		return loss.item(), perplexity.item()


# Setup Accelerator
_UpperCAmelCase          =						Accelerator()

# Parse configuration
_UpperCAmelCase          =						HfArgumentParser(EvaluationArguments)
_UpperCAmelCase          =						parser.parse_args()
set_seed(args.seed)

# Logging
_UpperCAmelCase          =						logging.getLogger(__name__)
logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)

# Load model and tokenizer
_UpperCAmelCase          =						AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_UpperCAmelCase          =						AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_UpperCAmelCase          =						create_dataloader(args)

# Prepare everything with our `accelerator`.
_UpperCAmelCase     ,_UpperCAmelCase          =						accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
_UpperCAmelCase     ,_UpperCAmelCase          =						evaluate(args)


logger.info(F"""loss/eval: {eval_loss}, perplexity: {perplexity}""")







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase          =						logging.get_logger(__name__)

_UpperCAmelCase          =						{
    """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/config.json""",
    """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/config.json""",
    """xlm-roberta-large-finetuned-conll02-dutch""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll02-spanish""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-english""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-german""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"""
    ),
}





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				lowerCamelCase_        =   '''xlm-roberta'''
				def __init__(		self						,      lowercase=3_0_5_2_2						,      lowercase=7_6_8						,      lowercase=1_2						,      lowercase=1_2						,      lowercase=3_0_7_2						,      lowercase="gelu"						,      lowercase=0.1						,      lowercase=0.1						,      lowercase=5_1_2						,      lowercase=2						,      lowercase=0.02						,      lowercase=1E-12						,      lowercase=1						,      lowercase=0						,      lowercase=2						,      lowercase="absolute"						,      lowercase=True						,      lowercase=None						,      **lowercase						,      ):



						"""simple docstring"""





						super().__init__(pad_token_id=lowercase						,      bos_token_id=lowercase						,      eos_token_id=lowercase						,      **lowercase   )

						A_						:							Union[str, Any]      = vocab_size
						A_						:							int      = hidden_size
						A_						:							Dict      = num_hidden_layers
						A_						:							List[str]      = num_attention_heads
						A_						:							Tuple      = hidden_act
						A_						:							Optional[int]      = intermediate_size
						A_						:							Any      = hidden_dropout_prob
						A_						:							Tuple      = attention_probs_dropout_prob
						A_						:							int      = max_position_embeddings
						A_						:							Union[str, Any]      = type_vocab_size
						A_						:							List[str]      = initializer_range
						A_						:							Tuple      = layer_norm_eps
						A_						:							str      = position_embedding_type
						A_						:							Any      = use_cache
						A_						:							str      = classifier_dropout





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						if self.task == "multiple-choice":
								A_						:							Union[str, Any]      = {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
								A_						:							str      = {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]   )






